Comparison of staircase and asymmetrical before–after, control–impact (aBACI) experimental designs to test the effectiveness of stream restoration at increasing juvenile steelhead density
Bibliographic record
Abstract
Before–after, control–impact (BACI) experimental designs are commonly used in large-scale environmental experiments, but these designs can be confounded by location and time interactions. Staircase designs, where replicate treatments are staggered temporally, have been suggested as an alternative to BACI designs. We performed a simulation study based on data from an ongoing watershed-scale restoration experiment within three streams to test the effectiveness of adding large wood to increase habitat complexity and abundance and productivity of juvenile steelhead (Oncorhynchus mykiss). We compared the power of two asymmetric BACI (aBACI) designs with two staircase designs for detecting changes in the density of steelhead (fish·m–2). A staircase design where treatments were temporally staggered in one treatment section in each stream had the highest power and best precision, especially when the innate spatial and temporal variances of steelhead density were large. A traditional BACI performed the worst, and a variation on another BACI and staircase design had intermediate performance. Multistream staircase designs are also more logistically and economically feasible and can maximize learning by replicating experiments across different stream types.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".